class_docstr: torchaudio.models.RNNT()
  
  Recurrent neural network transducer (RNN-T) model.
  
  Note:
      To build the model, please use one of the factory functions.
  
  Args:
      transcriber (torch.nn.Module): transcription network.
      predictor (torch.nn.Module): prediction network.
      joiner (torch.nn.Module): joint network.
      attndim (int): TCPGen attention dimension
      biasing (bool): If true, use biasing, otherwise use standard RNN-T
      deepbiasing (bool): If true, use deep biasing by extracting the biasing vector
      embdim (int): dimension of symbol embeddings
      jointdim (int): dimension of the joint network joint dimension
      charlist (list): The list of word piece tokens in the same order as the output layer
      encoutdim (int): dimension of the encoder output vectors
      dropout_tcpgen (float): dropout rate for TCPGen
      tcpsche (int): The epoch at which TCPGen starts to train
      DBaverage (bool): If true, instead of TCPGen, use DBRNNT for biasing
code: def get_tcpgen_step(self, vy, trie, resettrie):
      new_tree = trie[0]
      if vy in [self.blank_idx]:
          new_tree = resettrie
      elif self.char_list[vy].endswith("▁"):
          if vy in new_tree and new_tree[vy][0] != {}:
              new_tree = new_tree[vy]
          else:
              new_tree = resettrie
      elif vy not in new_tree:
          new_tree = [{}]
      else:
          new_tree = new_tree[vy]
      return new_tree
  
imports: import math
  from typing import Dict, List, Optional, Tuple
  import torch
  from torchaudio.models import Conformer, RNNT
  from torchaudio.models.rnnt import _Joiner, _Predictor, _TimeReduction, _Transcriber
label_desc: Use image node_id 7 for calling the RNNTBiasing obj's underlying member method code with example usage: obj.get_tcpgen_step(vy, trie, resettrie) and returns: new_tree